基于链路预测的脑功能网络重构方法及应用  

Construction Method and Application of Brain Functional Network Based on Link Prediction

作　　者：李艺茹 薛家玥 王子健 杨鹏飞 相洁 LI Yiru;XUE Jiayue;WANG Zijian;YANG Pengfei;XIANG Jie(College of Information and Computer,Taiyuan University of Technology,Jinzhong 030600,China)

机构地区：[1]太原理工大学信息与计算机学院,山西晋中030600

出　　处：《太原理工大学学报》2023年第5期820-829,共10页Journal of Taiyuan University of Technology

基　　金：国家自然科学基金资助项目(61873178,61876124,61906130);山西省国际科技合作项目(201803D421047)。

摘　　要：【目的】传统的静息态功能磁共振成像脑网络构建方法可能存在虚假链接或者缺失边,网络表达不够准确,重复测量的稳定性有待进一步提高。【方法】基于3种局部信息指标和4种节点定义策略,将链路预测算法用于脑功能网络重构,并利用两组测试数据对重构网络拓扑指标的可靠性进行分析。【结果】实验结果表明,重构网络提高了重复测量的稳定性。此外,利用重构网络对阿尔兹海默症(alzheimer’s disease, AD)患者脑网络进行组间差异分析,结果发现重构网络存在显著的组间差异,符合已有研究结果。【Purposes】The brain functional network construction method of resting state functional magnetic resonance imaging(rs-fMRI)has been relatively mature,but the fMRI signal acquisition process is affected by acquisition equipment,subjects’own reasons,noise,and other factors.The traditional rs-fMRI brain network construction method may have false links or missing edges,the network expression accuracy and the stability of repeated measurement needs to be further improved.【Methods】In order to solve above problems,on the basis of three local information indexes and four node definition strategies,the link prediction algorithm is applied to brain function network reconstruction,and two groups of test data are used to analyze the reliability of the reconstructed network topology index.【Findings】The experimental results show that the reconstructed network improves the stability of repeated measurement.In addition,the inter group difference of brain network in patients with Alzheimer's disease(AD)is analyzed by using the reconstructed network.The results show that there are significant inter group differences in the reconstructed network,which is consistent with the existing research results.This study provides a certain reference for the technology of constructing a stable brain function net-work.

关 键 词：脑功能网络 链路预测 局部信息指标 重复测量稳定性 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]